NDLI: Plasma Spray Process Operating Parameters Optimization Based on Artificial Intelligence

Content Provider	Springer Nature Link
Author	Liu, T. Planche, M. P. Kanta, A. F. Deng, S. Montavon, G. Deng, K. Ren, Z. M.
Copyright Year	2013
Abstract	During plasma spray process, many intrinsic operating parameters allow tailoring in-flight particle characteristics (temperature and velocity) by controlling the plasma jet properties, thus affecting the final coating characteristics. Among them, plasma flow mass enthalpy, flow thermal conductivity, momentum density, etc. result from the selection of extrinsic operating parameters such as the plasma torch nozzle geometry, the composition and flow rate of plasma forming gases, the arc current intensity, beside the coupled relationships between those operating parameters make difficult in a full prediction of their effects on coating properties. Moreover, temporal fluctuations (anode wear for example) require “real time” corrections to maintain particle characteristic to targeted values. An expert system is built to optimize and control some of the main extrinsic operating parameters. This expert system includes two parts: (1) an artificial neural network (ANN) which predicts an extrinsic operating window and (2) a fuzzy logic controller (FLC) to control it. The paper details the general architecture of the system, discusses its limits and the typical characteristic times. The result shows that ANN can predict the characteristics of particles in-flight from coating porosity within maximal error 3 and 2 % in temperature and velocity respectively. And ANN also can predict the operating parameters from in-flight particle characteristics with maximal error 2.34, 4.80 and 8.66 % in current intensity, argon flow rate, and hydrogen flow rate respectively.
Starting Page	1025
Ending Page	1041
Page Count	17
File Format	PDF
ISSN	02724324
Journal	Plasma Chemistry and Plasma Processing
Volume Number	33
Issue Number	5
e-ISSN	15728986
Language	English
Publisher	Springer US
Publisher Date	2013-08-25
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Plasma spraying ANN FLC In-flight particle characteristics Prediction Inorganic Chemistry Mechanics Characterization and Evaluation of Materials Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Chemistry Surfaces, Coatings and Films Condensed Matter Physics Chemical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The Particle In-Flight Characteristics in Plasma Spraying Process Measured by Phase Doppler Anemometry (PDA)

An Extreme Learning Machine Algorithm to Predict the In-flight Particle Characteristics of an Atmospheric Plasma Spray Process

Study of Injection Angle and Carrier Gas Flow Rate Effects on Particles In-Flight Characteristics in Plasma Spray Process: Modeling and Experiments

A Perspective on Plasma Spray Technology

Improving the Generalization Ability of an Artificial Neural Network in Predicting In-Flight Particle Characteristics of an Atmospheric Plasma Spray Process

Atmospheric Plasma Spraying Evolution Since the Sixties Through Modeling, Measurements and Sensors

A Critical Assessment of Particle Temperature Distributions During Plasma Spraying: Numerical Studies for YSZ

Effect of Substrate and Its Shape on in-Flight Particle Characteristics in Suspension Plasma Spraying

Parameter Optimisation of a Vacuum Plasma Spraying Process Using Boron Carbide

Plasma Spray Process Operating Parameters Optimization Based on Artificial Intelligence

Similar Documents

The Particle In-Flight Characteristics in Plasma Spraying Process Measured by Phase Doppler Anemometry (PDA)

An Extreme Learning Machine Algorithm to Predict the In-flight Particle Characteristics of an Atmospheric Plasma Spray Process

Study of Injection Angle and Carrier Gas Flow Rate Effects on Particles In-Flight Characteristics in Plasma Spray Process: Modeling and Experiments

A Perspective on Plasma Spray Technology

Improving the Generalization Ability of an Artificial Neural Network in Predicting In-Flight Particle Characteristics of an Atmospheric Plasma Spray Process

Atmospheric Plasma Spraying Evolution Since the Sixties Through Modeling, Measurements and Sensors

A Critical Assessment of Particle Temperature Distributions During Plasma Spraying: Numerical Studies for YSZ

Effect of Substrate and Its Shape on in-Flight Particle Characteristics in Suspension Plasma Spraying

Parameter Optimisation of a Vacuum Plasma Spraying Process Using Boron Carbide

Plasma Spray Process Operating Parameters Optimization Based on Artificial Intelligence